자문화기술지의 치유구조 분석을 통한 인간-AI 협업 질적메타합성 방법론 탐색

초록

This study aims to explore healing structures in autoethnography and examine the methodological potential of Human–AI Collaborative Qualitative Meta-Synthesis (HAC-QMS). A human–AI collaborative meta-synthesis is conducted on 14 autoethnographic studies. The analysis proceeds through four stages: meaning unit construction, AI-driven pattern identification, human interpretive reconstruction, and conceptual integration. AI identifies recurring affective patterns and narrative structures, while the human researcher reinterprets these within a theoretical framework to derive meta-themes. This approach forms a dual analytic system combining pattern detection and interpretive theorization. Findings show that healing is not simple recovery but a processual transformation structured as “ontological rupture → affective suppression → self-fragmentation → expression and reflection → reconstruction → existential transformation” operating in cyclical and relational ways. HAC-QMS expands analytic scope through pattern detection while preserving theoretical depth through human interpretation. The study suggests that HAC-QMS enhances the systematicity and transparency of qualitative research and offers a methodological alternative for large-scale qualitative data. It also reconceptualizes healing as a process of ontological reconstruction, contributing to understandings of self-care and healing in educational, counseling, and social contexts.

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